The Random Forest algorithm [1]}, [2]} seeks to extend decision trees capabilities by simultaneously generating multiple, independent decision trees. For regression tasks, Random Forest will return the average of the outputs of each of the generated decision trees.
An advantage of Random Forest over decision trees is the reduction in the variance.
However, Random Forest often suffers from low interpretability.
